3 papers
math.OC2026
A Proximal-Gradient Method for Solving Regularized Optimization Problems with General Constraints
Frank E. Curtis, Xiaoyi Qu, Daniel P. Robinson
We propose, analyze, and test a proximal-gradient method for solving regularized optimization problems with general constraints. The method employs a decomposition strategy to comp…
math.OC2025
Active-Set Identification in Noisy and Stochastic Optimization
Frank E. Curtis, Daniel P. Robinson, Lara Zebiane
Identifying active constraints from a point near an optimal solution is important both theoretically and practically in constrained continuous optimization, as it can help identify…
cs.LG2024
Using Synthetic Data to Mitigate Unfairness and Preserve Privacy in Collaborative Machine Learning
Chia-Yuan Wu, Frank E. Curtis, Daniel P. Robinson
In distributed computing environments, collaborative machine learning enables multiple clients to train a global model collaboratively. To preserve privacy in such settings, a comm…